We build intelligent, fail-closed, verifiable systems for the AI agents and large language models that enterprise and industry now depend on—engineered to claim only what they can prove. Unlocking Agentic AI in risk sensitive and high-stakes applications.
We build fail-closed AI for autonomous systems. While standard models estimate safety probabilities, our platform generates machine-checkable, auditable certificates for every decision—eliminating hallucinations and edge-case failure modes in safety-critical deployments.
Mjölnir Fleet is an enterprise-scale AI governance system with fail-closed behaviour. Every decision is certifiable, auditable and replayable, so operators can prove what an agent did — and what it refused to do — long after the fact.
Open Mjölnir FleetSafe AI decision models, an algorithmic harness for any base learner that needs a fail-close decision layer, and RLVF post-training services for LLMs in risk-sensitive environments. Together they improve how agents recognize with certainty what they do not know, avoid policy violations, and work better with humans in unknown situations.
Explore modelsWe research algorithms under the fail-closed, Ω-certifiability paradigm, engineered for sustained deterministic behaviour under uncertainty. An AI that cannot state with certainty what it does not know is not fit for release in risk-sensitive systems — the core shortcoming of today's System-1 models that treat safety as a single probability number.
Read our researchAI agents and large language models are starting to take real actions — moving money, changing records, shipping code. We build for the places where a mistake there is not a bug, it is a liability, and "probably fine" isn't good enough.
Finance, healthcare, energy and critical infrastructure — where one unsupervised action by an AI agent can cost far more than any delay. Our systems make the call before the action runs, so nothing irreversible happens by accident.
Proof you can check, not a promise you have to trust. Machine-verifiable guarantees over systems — and large language models — far too large to review by hand, with evidence that travels with every answer.
LLMs now write more code than any team can read. We certify what they produced and flag what we cannot prove, keeping up with the volume instead of falling behind it.
Our thesis: advanced intelligence runs through self-knowledge. A system is trustworthy not for what it answers, but for what it can certify — and decline with certainty — about the edge of its own competence. We build AI that recognizes its own limits, publish the science openly so everyone can build on it, and ship the result as industry-grade systems.
Have a project in mind? Reach out and we'll explore how intelligent systems can transform your operations.
Whether you're exploring AI for the first time or scaling an existing initiative — our team is ready to help. We typically respond within 24 hours.